使用 TypeVar 创建 Self 类型变量，方便用户在 Pycharm 编辑器中链式调用校验方法

import functools
import inspect
from typing import TypeVar
def _error_prompt(value, exception_msg=None, rule_des=None, field=None):
    default = f'"{value}" is invalid.'
    prompt = exception_msg or rule_des
    prompt = f'{default} due to: {prompt}' if prompt else default
    prompt = f'{field} error: {prompt}' if field else prompt
    return prompt
def raise_exception(func):
    @functools.wraps(func)
    def wrapper(self, *args, **kwargs):
        bound_args = inspect.signature(func).bind(self, *args, **kwargs).arguments
        exception_msg = kwargs.get('exception_msg', None) or bound_args.get('exception_msg', None)
        error_prompt = _error_prompt(self.value, exception_msg, self._rule_des, self._field)
        result = func(self, *args, **kwargs)
        if not result:
            raise ValueError(error_prompt)
        return self
    return wrapper
class RaiseExceptionMeta(type):
    def __new__(cls, name, bases, dct):
        for key, value in dct.items():
            if isinstance(value, staticmethod):
                dct[key] = staticmethod(raise_exception(value.__func__))
            if isinstance(value, classmethod):
                dct[key] = classmethod(raise_exception(value.__func__))
            if inspect.isfunction(value) and not key.startswith("__"):
                dct[key] = raise_exception(value)
        return super().__new__(cls, name, bases, dct)
'''
- TypeVar 是 Python 中用于声明类型变量的工具
- 声明一个类型变量，命名为 'Self', 意思为表示类的实例类型
- bound 参数指定泛型类型变量的上界，即限制 'Self' 必须是 'Validator' 类型或其子类型
'''
Self = TypeVar('Self', bound='Validator')
class Validator(metaclass=RaiseExceptionMeta):
    def __init__(self, value, field=None, rule_des=None):
        self.value = value
        self._field = field
        self._rule_des = rule_des
    def is_string(self, exception_msg=None) -> Self:
        """
        将返回类型注解定义为 Self, 支持编辑器如 pycharm 智能提示链式调用方法，如：Validator(input).is_string().is_not_empty()
        - 在 Python 3.5 中引入类型注解的概念，用于支持类型提示和静态类型检查；
      - 类型注解允许开发者在函数参数、返回值和变量上添加类型信息，但是在运行时，Python 解释器不会检查这些注解是否正确；
      - 它们主要用于提供给静态类型检查器或代码编辑器进行检查，以提供更好的代码提示和错误检测；
      - Python 运行时并不强制执行这些注解，Python 依然是一门动态类型的语言。
        - 本方法中：
            - 返回值类型为 bool 类型，用于与装饰器函数 raise_exception 配合使用，校验 self.value 是否通过；
            - 为了支持编辑器如 pycharm 智能识别链式调用方法，如：Validator(input).is_string().is_not_empty()，将返回类型注解定义为 Self； 
            - Self, 即 'Validator', 由 Self = TypeVar('Self', bound='Validator') 定义；
        """
        return isinstance(self.value, str)
    def is_not_empty(self, exception_msg=None) -> Self:
        return bool(self.value)

import pytest
from atme.demo.validator_v4.validator import Validator
def test_validator_01():
    """
    在 Validator 实例化时，不给 field、rule_des 传值; 在校验方法中，不给 exception_msg 传值
    """
    validator = Validator('Jane')
    assert validator.is_string().is_not_empty()
    with pytest.raises(ValueError) as exc_info:
        validator = Validator(123)
        validator.is_string().is_not_empty()
    assert 'invalid' in str(exc_info.value)
    print(exc_info.value)  # 输出: "123" is invalid.
def test_validator_02():
    """
    在 Validator 实例化时，给 field、rule_des 传值
    """
    validator = Validator('Jane', field='name', rule_des='name must be string from rule des.')
    assert validator.is_string().is_not_empty()
    with pytest.raises(ValueError) as exc_info:
        validator = Validator(123, field='name', rule_des='name must be string from rule des.')
        validator.is_string().is_not_empty()
    assert 'name must be string from rule des.' in str(exc_info.value)
    print(exc_info.value)  # 输出: name error: "123" is invalid. due to: name must be string from rule des.
def test_validator_03():
    """
    在 Validator 实例化时，给 field、rule_des 传值; 在校验方法中，给 exception_msg 传值
    """
    validator = Validator('Jane', field='name', rule_des='name must be string from rule des.')
    assert validator.is_string().is_not_empty()
    with pytest.raises(ValueError) as exc_info:
        validator = Validator(123, field='name', rule_des='name must be string from rule des.')
        validator.is_string('name must be string from method exception msg.').is_not_empty()
    assert 'name must be string from method exception msg.' in str(exc_info.value)
    print(exc_info.value)  # 输出: "123" is invalid due to "name error: name must be string from method exception msg."
def test_validator_04():
    """
    field_name 为空
    """
    validator = Validator('Jane', rule_des='name must be string from rule des.')
    assert validator.is_string().is_not_empty()
    with pytest.raises(ValueError) as exc_info:
        validator = Validator(123, rule_des='name must be string from rule des.')
        validator.is_string('name must be string from method exception msg.').is_not_empty()
    assert 'name must be string from method exception msg.' in str(exc_info.value)
    print(exc_info.value)  # 输出: "123" is invalid due to "name must be string from method exception msg."

============================= test session starts =============================
collecting ... collected 4 items
test_validator.py::test_validator_01 PASSED                              [ 25%]"123" is invalid.
test_validator.py::test_validator_02 PASSED                              [ 50%]name error: "123" is invalid. due to: name must be string from rule des.
test_validator.py::test_validator_03 PASSED                              [ 75%]name error: "123" is invalid. due to: name must be string from method exception msg.
test_validator.py::test_validator_04 PASSED                              [100%]"123" is invalid. due to: name must be string from method exception msg.
============================== 4 passed in 0.01s ==============================